Open AccessSegment attention‐guided part‐aligned network for person re‐identification
Author(s) -
Wang Wen,
Liu Yongwen,
An Gaoyun
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12178
Subject(s) - computer science , parsing , identification (biology) , artificial intelligence , attention network , dimension (graph theory) , image (mathematics) , computer vision , calibration , pattern recognition (psychology) , scale (ratio) , cartography , mathematics , statistics , botany , pure mathematics , biology , geography
Part misalignment of the human body caused by complex variations in viewpoint and pose poses a fundamental challenge to person re‐identification. This letter examines Res2Net as the backbone network to extract multi‐scale appearance features. At the same time, it uses the human parsing model to extract part features, which can be used as an attention stream to guide part features re‐calibration from the spatial dimension. Additionally, in order to ensure the diversity of features, SAG‐PAN effectively integrates the global appearance features of person image with part fine‐grained features. The experimental results on the Market‐1501, DukeMTMC‐reID and CUHK03 datasets show that the proposed SAG‐PAN achieved superior performance against the existing state‐of‐the‐art methods.